5. ANALYSIS Of ThE IMPACT Of ThE EPA ON ThE KENYAN ECONOMY
5.5. MONITOrING INDICATOrS
In order to track the evolution of gender disparities over time and eventually relate it to implementation of trade reforms (and accompanying measures if they have been implemented), it is necessary to build a consistent monitoring framework. A monitoring
framework is a tool that would help countries assess the evolution of gender inequalities before and after the trade reforms. As such, it would contribute to the development and implementation of strategies that adequately address the obstacles on the path to gender equality.
V. AnAlysis of the impAct of the epA on the KenyAn economy 31
Table 17 proposes a sample of monitoring indicators applied to Kenya based on data availability. The indicators provided in tables 17-21 inform about the situation prior to implementation of the EPA. Once the trade reforms have been implemented, the indicators should be updated to make it possible to track and correlate the evolution in gender inequalities with the trade reforms.
As discussed in previous sections, gender inequality can take multiple forms, so it is crucial to take a variety of dimensions into account in the monitoring indicators. for instance, in the workplace, gender inequalities may surface through differences in wages or the composition of labour, but also in working conditions, such as the differences in the type of labour contract (for instance, are short-term contracts more prevalent for female workers than for men?).
Based on this framework, the tables in the sub-section
Occupation
Male wages (net hourly, Kenyan
shillings)
Female wages (net hourly, Kenyan shillings)
Gender wage gap (per cent)
Finance managers 154.55 120.35 22.14
Human resource managers 72.69 98.06 -34.89
Restaurant managers 57.77 53.77 6.94
Accounting associate professionals 113.07 103.71 8.28
Secretaries (general) 41.94 61.69 -47.09
Travel consultants and clerks 51.41 41.82 18.66
Travel guides 54.85 50.80 7.38
Cooks 41.07 27.14 33.92
Waiters 26.38 21.30 19.24
Field crop and vegetable growers 25.89 36.15 -39.61
Forestry and related workers 64.66 32.87 49.17
Subsistence mixed crop and livestock 20.38 15.23 25.27
Carpenters and joiners 45.28 43.18 4.65
Car, taxi, and van drivers 40.96 n.a. n.a
Heavy truck and lorry drivers 61.01 31.13 48.97
Cleaners and helpers in offices, hotels 40.41 33.37 17.41
Crop farm labourers 26.76 31.68 -18.38
Building construction labourers 28.48 90.08 -216.33
Table 19. Monitoring framework for gender inequalities in wages in Kenya, 2012
Source: Calculations by the UNCTAD secretariat based on Tijdens and Wanbugu (2012).
Note: The data were collected in Kenya from face-to-face interviews with a random sample of individuals within a predefined set of occupations. The data are replicated from the data collected in Section 3.3 and should serve as the benchmark to construct the monitoring framework across periods. The gender gap should be interpreted with caution, as it is calculated based on the average hourly wage within each occupation group, without taking into account the differences in terms of education, experience, age, etc.
between men and women. n.a.: not applicable.
that follows present the monitoring indicators that can be used to track the evolution of gender disparities in Kenya over time for the different dimensions that have been mentioned.
5.5.1. Monitoring indicators of gender inequalities in the workplace
Table 18 refers to the employment dimension, table 19 to wage inequalities, and table 20 to working conditions.
Table 18 highlights that women are under-represented in the workplace, as the gender gap is positive in all sectors with the notable exceptions of public administration, activities of households as employers, and health and social work activities.
Table 19 reveals that women tend to earn a lower wage than men in most occupations, as indicated
Female share Male share Gender gap Hours worked per week (2009)*
None 0.03 0.03 -4.5
1 to 14 hours 6.05 5 -12.2
15 to 29 hours 14.65 10.42 -30.3
30 to 39 hours 17.45 13.18 -22.7
40 to 48 hours 21.43 23.55 15.7
49 hours of more 27.86 36.71 29.6
Unknown 12.53 11.11
Total 100 100
Type of contract (2012)**
Permanent 76.95 76.34 -0.8
Non-permanent 22.52 23.34 3.51
Bargaining coverage (2012)**
Covered by collective agreement 40.28 30.71 -31.2
Member of a trade union 23.69 19.49 -21.6
Social coverage in the workplace (2012)**
Participation in a health insurance scheme 27.35 25.13 -8.8
Participation in a pension scheme 18.65 19.83 6
Access to child-care arrangement 3.94 3.82 -3.1
Table 20. Monitoring framework for gender inequalities in working conditions in Kenya (per cent)
Source: Calculations by the UNCTAD secretariat based on the following: *Kenya’s 2009 Population and Housing Census, available via the Integrated Public Use Microdata Series (IPUMS) International (Minnesota Population Center, 2015). The data are based on a representative sample of employed individuals; ** Tijdens and Wanbugu (2012), based on a representative sample of predefined occupations.
Note: The gender gap here corresponds to the relative gender gap applied to the proportion of men and women for each of the dimen-sions considered. For the number of weekly work hours, the gender gap is calculated using the absolute number of men (women) (not the share). The gender gap should be interpreted with caution, as it does not take into account other factors such as age, experience, education, etc. that could explain the difference between the working hours of men and women.
by the positive (and often large) gender wage gap.
Notable exceptions are human resource managers, secretaries, crop growers, and crop labourers. These occupations are, in fact, considered more female-oriented.21
As opposed to the previous dimensions considered, table 20 shows more nuance in the differences in working conditions between men and women in Kenya. for instance, among surveyed individuals, the proportion of women with permanent contracts is slightly higher than the proportion of men. Similarly, the proportion of women covered by a collective
agreement or part of a trade union is significantly higher than the proportion of men. However, this could be explained by the fact that women are more likely found in sectors where trade unions are more prevalent (i.e. the textile industry).
5.5.2. Monitoring indicators of gender inequalities in access to resources
Table 21 reveals that women in Kenya, on average, have less access to resources (land, financial services, bank account) than men, although the computed gender gaps are not significantly large.
V. AnAlysis of the impAct of the epA on the KenyAn economy 33
Female (per cent of persons
15+ years of age)
Male (per cent of persons
15+ years of age)
Gender gap (per cent) Property or land
Own property 7 30.2 0.77
Joint property 28.2 12.6 -1.24
Borrowed money…
In the past year 78.27 80.23 0.02
From a financial institution 6.79 12.82 0.47
From a private informal lender 5.56 7.67 0.27
From family or friend 62.33 58.35 -0.07
From a store by buying on credit 19.65 16.45 -0.19
To start, operate, or expand a farm or business 21.12 27.82 0.24
Use of bank account...
In a financial institution 51.94 58.92 0.12
In a financial institution for business purposes 9.12 16.09 0.43
Table 21. Monitoring framework for gender inequalities in access to resources in Kenya (per cent)
Source: Calculations by the UNCTAD secretariat based on the World Bank Gender Statistics Database, 2016. The data are for 2014, the latest year available in the survey.
Note: The gender gap here corresponds to the relative gender gap applied to the proportion of men and women for each of the dimensions considered.
NOTES
7 The Market Access regulation scheme provides duty-free, quota-free market access to the European Union for those products originating in those ACP states that do not benefit from an EBA regime and have concluded EPAs with the European Union. European Union regulation 1528/2007 governs the European Union preferential market access regime for ACP countries that have negotiated EPAs with the European Union. See http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2007:348:0001:0154:EN:PDf (accessed April 2017).
8 See Bridges Africa, EAC-EU EPA signing postponed as deliberations continue, 22 July 2016, International Centre for Trade and Sustainable Development. Available at http://www.ictsd.org/bridges-news/bridges-africa/
news/eac-eu-epa-signing-postponed-as-deliberations-continue (accessed April 2017).
9 Kenya Ministry of foreign Affairs press release, 28 September 2016. Available at http://www.mfa.go.ke/kenya-deposits-instruments-epas-ratification/ (accessed April 2017).
10 See Bridges Africa, Tanzanian parliament advises government not to sign EPA with EU, 17 November 2016,
International Centre for Trade and Sustainable Development. Available at:
http://www.ictsd.org/bridges-news/bridges-africa/news/tanzanian-parliament-advises-government-not-to-sign-epa-with-eu (accessed April 2017).
11 Collins, Maui, East Africa: EPAs Deal - East African Community undecided as second deadline elapses, 3 february 2017, allAfrica. Available at: http://allafrica.com/stories/201702030130.html (accessed April 2017).
12 The GSP provides a formal system of exemptions from the most favoured nation principle, thereby allowing world Trade Organization member countries to lower tariffs for a selected group of countries (like developing countries) without granting this advantage to other countries or requiring reciprocity.
13 for instance, in the textile sector, a “single transformation” (such as spinning, weaving, or assembly) is sufficient to obtain the origin label for a product, in contrast with the previously enforced “double transformation rule”
under which an operator would have to transform the yarn into a fabric and a fabric into clothing (European Commission, 2017).
14 Standard rules of origins distinguish between products eligible for special treatment based on whether they have been “wholly obtained” or “substantially transformed” in a given country. Substantial transformation occurs when imported products turn into manufacturing goods with a new name, character, or use. with the cumulation of origin, a product can meet the requirements of substantial transformation in more than one country.
15 The exclusion list includes the following products (non-exhaustive): live animals; meat and edible meat offal; fish and crustaceans, mollusks and other aquatic invertebrates; dairy produce; birds’ eggs; natural honey; edible products of animal origin; live trees and other plants; bulbs, roots and the like; cut flowers and ornamental foliage;
edible vegetables and certain roots and tubers; edible fruit and nuts; peel of citrus fruits or melons; coffee, tea, maté and spices; cereals; products of the milling industry; malt; starches; vegetable plaiting materials;
vegetable products; animal or vegetable fats and oils and their cleavage products; prepared edible fats; animal or vegetable waxes; preparations of meat, of fish or of crustaceans, mollusks or other aquatic invertebrates;
sugars and sugar confectionery; cocoa and cocoa preparations; preparations of cereals, flour, starch or milk;
pastry cooks’ products; preparations of vegetables, fruit, nuts or other parts of plants; miscellaneous edible preparations; beverages, spirits and vinegar; residues and waste from the food industries; prepared animal fodder; tobacco and manufactured tobacco substitutes; plastics and articles thereof; wood and articles of wood; cotton; man-made filaments; man-made staple fibres; footwear, gaiters and the like; parts of such articles; iron and steel; and articles of iron or steel (KHrC, 2015).
16 As explained in the previous section, we want to know what would happen if the EPA were implemented compared to the hypothetical situation in which there is no EPA and the GSP comes into force.
17 The CGE does not have a category corresponding to “wholesale and retail trade.” Therefore, this sector cannot be directly estimated. The products traded by women, however, are most likely incorporated in the other sectors included in the analysis (Kiriti-Nganga, 2015).
18 Carr (2008), Doss (2002), UNCTAD (2016b), and wa Gĩthĩnji et al. (2014).
19 The housing and population census was undertaken by the Kenyan Bureau of Economic Statistics and is based on a representative sample of working individuals.
20 for this purpose, we rely on the data from the Population and Housing Census, available via the Integrated Public Use Microdata Series (IPUMS) International (Minnesota Population Center, 2015). These data provide the most relevant information about the distribution of the population across economic activities (formal and informal). More specifically, we use the figures about the share of active women involved in informal activities to adjust the shares obtained from the Kenyan National Bureau of Statistics. The detailed computation of these adjusted figures is provided in the worksheet 2.
21 In Kenya, women’s participation in the building construction workforce is small; the gender wage gap for building construction labourers indicated in table 19 is based on a small sample and cannot be generalized.
VI. Trade and Gender Index 35